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A model-based insulin sensitivity parameter (SI) is often used in glucose-insulin system models to define the glycaemic response to insulin. As a parameter identified from clinical data, insulin sensitivity can be affected by blood glucose (BG) sensor error and measurement timing error, which can subsequently impact analyses or glycaemic variability during control. This study assesses the impact of both measurement timing and BG sensor errors on identified values of SI and its hour-to-hour variability within the ICING-type glucose-insulin system models. Retrospective clinical data was used from 270 patients of the Christchurch Hospital intensive care unit (ICU). An error model was created for the Arkray Super-Glucocard II glucometer used in Christchurch from manufacturer supplied data. Timing error was estimated from recent, computerised clinical data. Monte Carlo analysis was used to quantify the impact of these random errors by identifying SI profiles from data incorporating errors and comparing them to the ‘true’ SI profile (without additional errors) at each patient hour. To consolidate comparisons over the n = 100 Monte Carlo simulations, the width of the interquartile range (IQR) was used for percentage difference from the true SI level and for percentage hour-to-hour variability. The results of the study show that timing errors in isolation have little clinically significant impact on identified SI level or variability. The clinical impact of changes to SI level induced by combined sensor and timing errors is likely to be limited during glycaemic control. Identified values of SI were typically within 12% of the true value when influenced by both sources of error. In contrast, for variability, 95% of patient hours had an IQR of 34.9%, indicating that for half the simulations the hour-to-hour variability of SI was within ±17.5%. The results of this study indicate that the impact of sensor or timing errors on SI level is unlikely to be clinically significant. The effects are probably overshadowed by physiological factors arising from the critical condition of the patients or other under-modelled or un-modelled dynamics. In contrast, the impact of errors on hour-to-hour SI variability is more pronounced

Subjects

biomedical system modeling

simulation and visualization

control of physiological and clinical variables

Field of Research::11 - Medical and Health Sciences::1101 - Medical Biochemistry and Metabolomics